Rapid instructed task learning: A new window into the human brain's unique capacity for flexible cognitive control

Cognitive, Affective and Behavioral Neuroscience

Volumn 13, Issue 1, 2013, Pages 1-22

  Cole, Michael W ^a   Laurent, Patryk ^b   Stocco, Andrea ^c  

^a WASHINGTON UNIVERSITY   (United States)

^b JOHNS HOPKINS UNIVERSITY   (United States)

^c UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Animal models; Cognitive control; Cognitive flexibility; Compositionality; Computational model; Flexible cognitive control; Prefrontal cortex

Indexed keywords

ANIMAL; ANIMAL MODEL; BRAIN; COGNITION; HUMAN; INTELLIGENCE; LANGUAGE; LEARNING; PHYSIOLOGY; PREFRONTAL CORTEX; PSYCHOLOGICAL MODEL; REVIEW;

ANIMALS; BRAIN; COGNITION; HUMANS; INTELLIGENCE; LANGUAGE; LEARNING; MODELS, ANIMAL; MODELS, PSYCHOLOGICAL; PREFRONTAL CORTEX;

EID: 84871870365     PISSN: 15307026     EISSN: None     Source Type: Journal    
DOI: 10.3758/s13415-012-0125-7     Document Type: Review

References (128)

1. Anderson, J. R. (1976). Language, memory, and thought. Hillsdale, NJ: Erlbaum.
2. Avants, B., Schoenemann, P., & Gee, J. (2006). Lagrangian frame diffeomorphic image registration: Morphometric comparison of human and chimpanzee cortex. Medical Image Analysis, 10, 397-412. doi: 10.1016/j.media.2005.03.005
3. Azevedo, F. A. C., Carvalho, L. R. B., Grinberg, L. T., Farfel, J. M., Ferretti, R. E. L., Leite, R. E. P.,. Herculano-Houzel, S. (2009). Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. Journal of Comparative Neurology, 513, 532-541. doi: 10.1002/cne.21974
4. Badre, D. (2008). Cognitive control, hierarchy, and the rostro-caudal organization of the frontal lobes. Trends in Cognitive Sciences, 12, 193-200. doi: 10.1016/j.tics.2008.02.004
5. Badre, D., & D'Esposito, M. (2007). Functional magnetic resonance imaging evidence for a hierarchical organization of the prefrontal cortex. Journal of Cognitive Neuroscience, 19, 2082-2099. doi: 10.1162/jocn.2007.19.12. 2082
6. Badre, D., & D'Esposito, M. (2009). Is the rostro-caudal axis of the frontal lobe hierarchical? Nature Reviews Neuroscience, 10, 659-669. doi: 10.1038/nrn2667
7. Barbas, H., & Zikopoulos, B. (2007). The prefrontal cortex and flexible behavior. Neuroscientist, 13, 532-545. doi: 10.1177/1073858407301369
8. Barch, D. M., Braver, T. S., Carter, C. S., Poldrack, R. A., & Robbins, T. W. (2009). CNTRICS final task selection: Executive control. Schizophrenia Bulletin, 35, 115-135. doi: 10.1093/schbul/sbn154
9. Barch, D. M., Carter, C. S., Braver, T. S., Sabb, F. W., Macdonald, A., Noll, D. C., & Cohen, J. D. (2001). Selective deficits in prefrontal cortex function in medication-naive patients with schizophrenia. Archives of General Psychiatry, 58, 280-288.
10. Biederman, I. (1987). Recognition-by-components: A theory of human image understanding. Psychological Review, 94, 115-147. doi: 10.1037/0033-295X.94.2. 115
11. Billing, D. (2007). Teaching for transfer of core/key skills in higher education: Cognitive skills. Higher Education, 53, 483-516. doi: 10.1007/s10734-005-5628-5
12. Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108, 624-652. doi: 10.1037/0033-295X.108.3.624
13. Botvinick, M. M. (2008). Hierarchical models of behavior and prefrontal function. Trends in Cognitive Sciences, 12, 201-208. doi: 10.1016/j.tics.2008. 02.009
14. Braver, T. S., & Cohen, J. D. (2000). On the control of control: The role of dopamine in regulating prefrontal function and working memory. In S. Monsell & J. Driver (Eds.), Control of cognitive processes: Attention and performance XVIII (pp. 713-737). Cambridge, MA: MIT Press.
15. Bugmann, G. (2011). Modeling fast stimulus-response association learning along the occipito-parieto-frontal pathway following rule instructions. Brain Research, 1-17. doi: 10.1016/j.brainres.2011.09.028
16. Byrne, R. W., & Russon, A. E. (1998). Learning by imitation: A hierarchical approach. Behavioral and Brain Sciences, 21, 667-684.
17. Chein, J. M., & Schneider, W. (2005). Neuroimaging studies of practice-related change: fMRI and meta-analytic evidence of a domain-general control network for learning. Cognitive Brain Research, 25, 607-623. doi: 10.1016/j.cogbrainres.2005.08.013
18. Chein, J. M., & Schneider, W. (2012). The brain's learning and control architecture. Current Directions in Psychological Science, 21, 78-84. doi: 10.1177/0963721411434977
19. Cohen-Kdoshay, O., & Meiran, N. (2009). The representation of instructions operates like a prepared reflex: Flanker compatibility effects found in first trial following S-R instructions. Experimental Psychology, 56, 128-133. doi: 10.1027/1618-3169.56.2.128
20. Cole, M. W. (2009). The biological basis of rapid instructed task learning. Unpublished dissertation, University of California, Berkeley, CA.
21. Cole, M. W., Anticevic, A., Repovs, G., & Barch, D. (2011a). Variable global dysconnectivity and individual differences in schizophrenia. Biological Psychiatry, 70, 43-50. doi: 10.1016/j.biopsych.2011.02.010
22. Cole, M. W., Bagic, A., Kass, R., & Schneider, W. (2010a). Prefrontal dynamics underlying rapid instructed task learning reverse with practice. Journal of Neuroscience, 30, 14245-14254. doi: 10.1523/JNEUROSCI.1662-10.2010
23. Cole, M. W., & Braver, T. S. (2012). Switching between novel tasks: Evidence for a distinct task set formation process. Manuscript submitted for publication.
24. Cole, M. W., Etzel, J. A., Zacks, J. M., Schneider, W., & Braver, T. S. (2011b). Rapid transfer of abstract rules to novel contexts in human lateral prefrontal cortex. Frontiers in Human Neuroscience, 5, 142. doi: 10.3389/fnhum.2011.00142
25. Cole, M. W., Pathak, S., & Schneider, W. (2010b). Identifying the brain's most globally connected regions. NeuroImage, 49, 3132-3148. doi: 10.1016/j.neuroimage.2009.11.001
26. Cole, M. W., & Schneider, W. (2007). The cognitive control network: Integrated cortical regions with dissociable functions. NeuroImage, 37, 343-360. doi: 10.1016/j.neuroimage.2007.03.071
27. Cole, M. W., Yarkoni, T., Repovs, G., Anticevic, A., & Braver, T. S. (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. Journal of Neuroscience, 32, 8988-8999. doi: 10.1523/Jneurosci. 0536-12.2012
28. Cole, M. W., Yeung, N., Freiwald, W. A., & Botvinick, M. (2009). Cingulate cortex: Diverging data from humans and monkeys. Trends in Neurosciences, 32, 566-574. doi: 10.1016/j.tins.2009.07.001
29. Conway, A. R. A., & Engle, R. W. (1996). Individual differences in working memory capacity: More evidence for a general capacity theory. Memory, 4, 577-590. doi: 10.1080/741940997
30. Cromer, J. A., Machon, M., & Miller, E. K. (2011). Rapid association learning in the primate prefrontal cortex in the absence of behavioral reversals. Journal of Cognitive Neuroscience, 23, 1823-1828. doi: 10.1162/jocn.2010.21555
31. Cromer, J. A., Roy, J. E., & Miller, E. K. (2010). Representation of multiple, independent categories in the primate prefrontal cortex. Neuron, 66, 796-807. doi: 10.1016/j.neuron.2010.05.005
32. Croxson, P. L., Kyriazis, D. A., & Baxter, M. G. (2011). Cholinergic modulation of a specific memory function of prefrontal cortex. Nature Neuroscience, 14, 1510-1512. doi: 10.1038/nn.2971
33. Dehaene, S., Kerszberg, M., & Changeux, J. P. (1998). A neuronal model of a global workspace in effortful cognitive tasks. Proceedings of the National Academy of Sciences, 95, 14529-14534.
34. Doll, B. B., Jacobs, W. J., Sanfey, A. G., & Frank, M. J. (2009). Instructional control of reinforcement learning: A behavioral and neurocomputational investigation. Brain Research, 1299, 74-94. doi: 10.1016/j.brainres.2009.07.007
35. Dosenbach, N., Fair, D., Miezin, F., Cohen, A., Wenger, K., Dosenbach, R.,. Raichle, M. (2007). Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences, 104, 11073-11078. doi: 10.1073/pnas.0704320104
36. Dosenbach, N. U. F., Visscher, K. M., Palmer, E. D., Miezin, F. M., Wenger, K. K., Kang, H. C.,. Petersen, S. E. (2006). A core system for the implementation of task sets. Neuron, 50, 799-812. doi: 10.1016/j.neuron.2006.04. 031
37. Dumontheil, I., Thompson, R., & Duncan, J. (2011). Assembly and use of new task rules in fronto-parietal cortex. Journal of Cognitive Neuroscience, 23, 168-182. doi: 10.1162/jocn.2010.21439
38. Duncan, J. (2001). An adaptive coding model of neural function in prefrontal cortex. Nature Reviews Neuroscience, 2, 820-829. doi: 10.1038/35097575
39. Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: Mental programs for intelligent behaviour. Trends in Cognitive Sciences, 14, 172-179. doi: 10.1016/j.tics.2010.01.004
40. Duncan, J., Burgess, P., & Emslie, H. (1995). Fluid intelligence after frontal lobe lesions. Neuropsychologia, 33, 261-268.
41. Duncan, J., & Owen, A. (2000). Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends in Neurosciences, 23, 475-483.
42. Duncan, J., Schramm, M., Thompson, R., & Dumontheil, I. (2012). Task rules, working memory, and fluid intelligence. Psychonomic bulletin & review. doi: 10.3758/s13423-012-0225-y
43. Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences, 102, 9673-9678. doi: 10.1073/pnas.0504136102
44. Freedman, D. J., Riesenhuber, M., Poggio, T., & Miller, E. K. (2001). Categorical representation of visual stimuli in the primate prefrontal cortex. Science, 291, 312-316. doi: 10.1126/science.291.5502.312
45. Fries, P. (2005). A mechanism for cognitive dynamics: Neuronal communication through neuronal coherence. Trends in Cognitive Sciences, 9, 474-480. doi: 10.1016/j.tics.2005.08.011
46. Fuster, J. M. (2001). The prefrontal cortex - an update: Time is of the essence. Neuron, 30, 319-333. doi: 10.1016/S0896-6273(01)00285-9
47. Fuster, J. M., Bauer, R., & Jervey, J. (1985). Functional interactions between inferotemporal and prefrontal cortex in a cognitive task. Brain Research, 330, 299-307. doi: 10.1016/0006-8993(85)90689-4
48. Gale, C. R., Batty, G. D., Tynelius, P., Deary, I. J., & Rasmussen, F. (2010). Intelligence in early adulthood and subsequent hospitalization for mental disorders. Epidemiology, 21, 70-77. doi: 10.1097/EDE.0b013e3181c17da8
49. Gentner, D., & Colhoun, J. (2010). Analogical processes in human thinking and learning. In B. M. Glatzeder, V. Goel, & A. von Müller (Eds.), Towards a theory of thinking: Building blocks for a conceptual framework (pp. 35-48). Berlin, Germany: Springer. doi: 10.1007/978-3-642-03129-8-3
50. Gentner, D., Loewenstein, J., & Thompson, L. (2003). Learning and transfer: A general role for analogical encoding. Journal of Educational Psychology, 95, 393. doi: 10.1037/0022-0663.95.2.393
51. Gentner, D., & Medina, J. (1998). Similarity and the development of rules. Cognition, 65, 263-297.
52. Gottfredson, L., & Saklofske, D. H. (2009). Intelligence: Foundations and issues in assessment. Canadian Psychology, 50, 183-195. doi: 10.1037/a0016641
53. Gray, E., & Tall, D. (2007). Abstraction as a natural process of mental compression. Mathematics Education Research Journal, 19, 23-40.
54. Hartstra, E., Kühn, S., Verguts, T., & Brass, M. (2011). The implementation of verbal instructions: An fMRI study. Human Brain Mapping, 32, 1811-1824. doi: 10.1002/hbm.21152
55. Hasselmo, M. E., & Stern, C. E. (2006). Mechanisms underlying working memory for novel information. Trends in Cognitive Sciences, 10, 487-493. doi: 10.1016/j.tics.2006.09.005
56. Haynes, J.-D., Sakai, K., Rees, G., Gilbert, S., Frith, C., & Passingham, R. E. (2007). Reading hidden intentions in the human brain. Current Biology, 17, 323-328. doi: 10.1016/j.cub.2006.11.072
57. Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. New York, NY: Wiley.
58. Henson, R. (2005). What can functional neuroimaging tell the experimental psychologist? Quarterly Journal of Experimental Psychology, 58A, 193-233. doi: 10.1080/02724980443000502
59. Heyes, C. (2001). Causes and consequences of imitation. Trends in Cognitive Sciences, 5, 253-261.
60. Hickman, J. M., Rogers, W. A., & Fisk, A. D. (2007). Training older adults to use new technology. Journals of Gerontology, 62B, P77-P84.
61. Holmes, N. (2005). Wordless diagrams. New York, NY: Bloomsbury.
62. Hummel, J. E., Holyoak, K. J., Green, C., Doumas, L. A. A., Devnich, D., Kittur, A., & Kalar, D. J. (2004). A solution to the binding problem for compositional connectionism. In S. D. Levy & R. Gayler (Eds.), Compositional connectionism in cognitive science: Papers from the AAAI Fall Symposium (pp. 31-34). New York, NY: ACM.
63. Jun, J. K., Miller, P., Hernandez, A., Zainos, A., Lemus, L., Brody, C. D., & Romo, R. (2010). Heterogenous population coding of a short-term memory and decision task. Journal of Neuroscience, 30, 916-929. doi: 10.1523/JNEUROSCI.2062-09.2010
64. Kiehl, K., Liddle, P., Smith, A., Mendrek, A., Forster, B., & Hare, R. (1999). Neural pathways involved in the processing of concrete and abstract words. Human Brain Mapping, 7, 225-233.
65. Kieras, D., & Bovair, S. (1986). The acquisition of procedures from text: A production-system analysis of transfer of training. Journal of Memory and Language, 25, 507-524.
66. Koechlin, E., Ody, C., & Kouneiher, F. (2003). The architecture of cognitive control in the human prefrontal cortex. Science, 302, 1181-1185. doi: 10.1126/science.1088545
67. Koenen, K. C., Moffitt, T. E., Roberts, A. L., Martin, L. T., Kubzansky, L., Harrington, H.,. Caspi, A. (2009). Childhood IQ and adult mental disorders: A test of the cognitive reserve hypothesis. American Journal of Psychiatry, 166, 50-57. doi: 10.1176/appi.ajp.2008.08030343
68. Lebiere, C., & Anderson, J. (1993). A connectionist implementation of the ACT-R production system. In W. Kintsch (Ed.), Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society (pp. 635-640). Hillsdale, NJ: Erlbaum.
69. Luria, A. R. (1973). The frontal lobes and the regulation of behavior. In K. H. Pribram & A. R. Luria (Eds.), Psychophysiology of the frontal lobes (pp. 3-28). New York, NY: Academic Press.
70. Luria, A. R., Pribram, K. H., & Homskaya, E. (1964). An experimental analysis of the behavioral disturbance produced by a left frontal arachnoidal endothelioma (meningioma). Neuropsychologia, 2, 257-280.
71. Lynch, M. (2004). Long-term potentiation and memory. Physiological Reviews, 84, 87-136.
72. Mayr, U., & Kliegl, R. (2000). Task-set switching and long-term memory retrieval. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 1124-1140. doi: 10.1037/0278-7393.26.5.1124
73. Mcnab, F., & Klingberg, T. (2008). Prefrontal cortex and basal ganglia control access to working memory. Nature Neuroscience, 11, 103-107. doi: 10.1038/nn2024
74. Meiran, N., Cole, M. W., & Braver, T. S. (2012). When planning results in loss of control: Intention-based reflexivity and working-memory. Frontiers in Human Neuroscience, 6, 104. doi: 10.3389/fnhum.2012.00104
75. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167-202. doi: 10.1146/annurev.neuro.24.1.167
76. Milner, B. (1964). Some effects of frontal lobectomy in man. In J. M. Warren & K. Akert (Eds.), The frontal granular cortex and behavior. New York, NY: McGraw Hill.
77. Milner, B. (1965). Visually-guided maze learning in man: Effects of bilateral hippocampal, bilateral frontal, and unilateral cerebral lesions. Neuropsychologia, 3, 317-338.
78. Monsell, S. (1996). Control of mental processes. In V. Bruce (Ed.), Unsolved mysteries of the mind: Tutorial essays in cognition (pp. 93-148). Hove, U.K.: Erlbaum.
79. Monsell, S. (2003). Task switching. Trends in Cognitive Sciences, 7, 134-140. doi: 10.1016/S1364-6613(03)00028-7
80. Muhammad, R., Wallis, J. D., & Miller, E. K. (2006). A comparison of abstract rules in the prefrontal cortex, premotor cortex, inferior temporal cortex, and striatum. Journal of Cognitive Neuroscience, 18, 974-989. doi: 10.1162/jocn.2006.18.6.974
81. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.
82. Noelle, D., & Cottrell, G. (1996). Modeling interference effects in instructed category learning. In G. W. Cottrell (Ed.), Proceedings of the 18th Annual Conference of the Cognitive Science Society (pp. 475-480). Hillsdale, NJ: Erlbaum.
83. Norman, K., Polyn, S., Detre, G., & Haxby, J. (2006). Beyond mind-reading: Multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10, 424-430. doi: 10.1016/j.tics.2006.07.005
84. Nusbaum, E. C., & Silvia, P. J. (2011). Are intelligence and creativity really so different? Intelligence, 39, 36-45. doi: 10.1016/j.intell.2010.11.002
85. O'Reilly, R. C., Braver, T. J., & Cohen, J. D. (1999). A biologically based computational model of working memory. In A. Miyake & P. Shah (Eds.), Models of working memory: Mechanisms of active maintenance and executive control (pp. 375-411). Cambridge, MA: Cambridge University Press.
86. O'Reilly, R. C., Busby, R. S., & Soto, R. (2003). Three forms of binding and their neural substrates: Alternatives to temporal synchrony. In A. Cleeremans (Ed.), The unity of consciousness: Binding, integration, and dissociation (pp. 168-192). Oxford, U.K.: Oxford University Press.
87. O'Reilly, R. C., & Frank, M. J. (2006). Making working memory work: A computational model of learning in the prefrontal cortex and basal ganglia. Neural Computation, 18, 283-328. doi: 10.1162/089976606775093909
88. O'Reilly, R. C., & Rudy, J. W. (2001). Conjunctive representations in learning and memory: Principles of cortical and hippocampal function. Psychological Review, 108, 311-345.
89. Oberauer, K. (2009). Design for a working memory. In B. H. Ross (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 51, pp. 45-100). San Diego, CA: Elsevier Academic Press. doi: 10.1016/S0079-7421(09)51002-X
90. Pasupathy, A., & Miller, E. K. (2005). Different time courses of learning-related activity in the prefrontal cortex and striatum. Nature, 433, 873-876. doi: 10.1038/nature03287
91. Pouget, P., Emeric, E. E., Stuphorn, V., Reis, K., & Schall, J. D. (2005). Chronometry of visual responses in frontal eye field, supplementary eye field, and anterior cingulate cortex. Journal of Neurophysiology, 94, 2086-2092. doi: 10.1152/jn.01097.2004
92. Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A.,. Petersen, S. E. (2011). Functional network organization of the human brain. Neuron, 72, 665-678. doi: 10.1016/j.neuron.2011.09.006
93. Rabbitt, P. (1997). Methodology of frontal and executive function. Hove, U.K.: Psychology Press.
94. Ramamoorthy, A., & Verguts, T. (2012). Word and deed: A computational model of instruction following. Brain Research, 1-12. doi: 10.1016/j.brainres. 2011.12.025
95. Rendell, L., Boyd, R., Cownden, D., Enquist, M., Eriksson, K., Feldman, M. W.,. Laland, K. N. (2010). Why copy others? Insights from the social learning strategies tournament. Science, 328, 208-213. doi: 10.1126/science.1184719
96. Rendell, L., Fogarty, L., Hoppitt, W. J. E., Morgan, T. J. H., Webster, M. M., & Laland, K. N. (2011). Cognitive culture: Theoretical and empirical insights into social learning strategies. Trends in Cognitive Sciences, 15, 68-76. doi: 10.1016/j.tics.2010.12.002
97. Reverberi, C., Görgen, K., & Haynes, J.-D. (2012). Compositionality of rule representations in human prefrontal cortex. Cerebral Cortex, 22, 1237-1246. doi: 10.1093/cercor/bhr200
98. Reynolds, J. R., O'Reilly, R. C., Cohen, J. D., & Braver, T. S. (2012). The function and organization of lateral prefrontal cortex: A test of competing hypotheses. PLoS ONE, 7, e30284. doi: 10.1371/journal.pone.0030284. t002
99. Rigotti, M., Rubin, D. B. D., Wang, X.-J., & Fusi, S. (2010). Internal representation of task rules by recurrent dynamics: The importance of the diversity of neural responses. Frontiers in Computational Neuroscience, 4, 24. doi: 10.3389/fncom.2010.00024
100. Ritter, S., Anderson, J. R., Koedinger, K. R., & Corbett, A. (2007). Cognitive Tutor: Applied research in mathematics education. Psychonomic Bulletin & Review, 14, 249-255. doi: 10.3758/BF03194060
101. Roy, J. E., Riesenhuber, M., Poggio, T., & Miller, E. K. (2010). Prefrontal cortex activity during flexible categorization. Journal of Neuroscience, 30, 8519-8528. doi: 10.1523/JNEUROSCI.4837-09.2010
102. Rubin, O., & Meiran, N. (2005). On the origins of the task mixing cost in the cuing task-switching paradigm. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 1477-1491. doi: 10.1037/0278-7393.31.6.1477
103. Ruge, H., Jamadar, S., Zimmermann, U., & Karayanidis, F. (2011). The many faces of preparatory control in task switching: Reviewing a decade of fMRI research. Human Brain Mapping. doi: 10.1002/hbm.21420
104. Ruge, H., & Wolfensteller, U. (2010). Rapid formation of pragmatic rule representations in the human brain during instruction-based learning. Cereb Cortex, 20, 1656-1667. doi: 10.1093/cercor/bhp228
105. Rumelhart, D. E., McClelland, J. L., & the PDP Research Group. (1986). Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 1). Cambridge, MA: MIT Press, Bradford Books.
106. Savage-Rumbaugh, E., Murphy, J., Sevcik, R., Brakke, K., Williams, S., Rumbaugh, D., & Bates, E. (1993). Language comprehension in ape and child. Monographs of the Society for Research in Child Development, 58, 1-222.
107. Schneider, W., & Chein, J. (2003). Controlled and automatic processing: Behavior, theory, and biological mechanisms. Cognitive Science, 27, 525-559.
108. Schneider, W., & Oliver, W. L. (1991). An instructable connectionist/control architecture: Using rule-based instructions to accomplish connectionist learning in a human time scale. In K. VanLehn (Ed.), Architectures for intelligence: The Twenty-Second Carnegie Mellon Symposium on Cognition (pp. 113-146). Hillsdale, NJ: Erlbaum.
109. Schneider, W., & Shiffrin, R. M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention. Psychological Review, 84, 1-66. doi: 10.1037/0033-295X.84.1.1
110. Seger, C. A., & Miller, E. K. (2010). Category learning in the brain. Annual Review of Neuroscience, 33, 203-219. doi: 10.1146/annurev.neuro.051508. 135546
111. Semendeferi, K., Armstrong, E., Schleicher, A., Zilles, K., & Van Hoesen, G. W. (2001). Prefrontal cortex in humans and apes: A comparative study of area 10. American Journal of Physical Anthropology, 114, 224-241. doi: 10.1002/1096-8644(200103)114:3<224::AID-AJPA1022>3.0.CO;2-I
112. Silvia, P. J., & Beaty, R. E. (2012). Making creative metaphors: The importance of fluid intelligence for creative thought. Intelligence, 40, 343-351. doi: 10.1016/j.intell.2012.02.005
113. Singley, M. K., & Anderson, J. R. (1989). The transfer of cognitive skill (Vol. 9). Cambridge, MA: Harvard University Press.
114. Squire, L. R. (2009). The legacy of patient H.M. for neuroscience. Neuron, 61, 6-9. doi: 10.1016/j.neuron.2008.12.023
115. Stocco, A., Lebiere, C., & Anderson, J. R. (2010a). Conditional routing of information to the cortex: A Model of the basal ganglia's role in cognitive coordination. Psychological Review, 117, 541-574. doi: 10.1037/a0019077
116. Stocco, A., Lebiere, C., O'Reilly, R. C., & Anderson, J. R. (2010). The role of the basal ganglia-anterior prefrontal circuit as a biological instruction interpreter. In Biologically inspired cognitive architectures 2010 (pp. 153-162). Amsterdam, The Netherlands: IOS Press.
117. Stocco, A., Lebiere, C., O'Reilly, R. C., & Anderson, J. R. (2012). Distinct contributions of the caudate nucleus, rostral prefrontal cortex, and parietal cortex to the execution of instructed tasks. Cognitive, Affective, & Behavioral Neuroscience. doi: 10.3758/s13415-012-0117-7
118. Sutton, R., & Barto, A. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
119. Turing, A. M. (1937). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 42(Suppl. 2), 230-265. doi: 10.1112/plms/s2-42.1.230
120. Turing, A. (1950). Computing machinery and intelligence. Mind, 49, 433-460.
121. VanLehn, K., Jones, R. M., & Chi, M. T. H. (1992). A model of the self-explanation effect. Journal of the Learning Sciences, 2, 1-59.
122. Verrico, C. D., Liu, S., Asafu-Adjei, J. K., Sampson, A. R., Bradberry, C. W., & Lewis, D. A. (2011). Acquisition and baseline performance of working memory tasks by adolescent rhesus monkeys. Brain Research, 1378, 91-104. doi: 10.1016/j.brainres.2010.12.081
123. Wager, T. D., Jonides, J., & Reading, S. (2004). Neuroimaging studies of shifting attention: A meta-analysis. NeuroImage, 22, 1679-1693. doi: 10.1016/j.neuroimage.2004.03.052
124. Wallis, J. D., Anderson, K. C., & Miller, E. K. (2001). Single neurons in prefrontal cortex encode abstract rules. Nature, 411, 953-956. doi: 10.1038/35082081
125. Wormeli, R. (2009). Metaphors & analogies: Power tools for teaching any subject: Stenhouse Pub.
126. Yeung, N., & Monsell, S. (2003). The effects of recent practice on task switching. Journal of Experimental Psychology Human Perception and Performance, 29, 919-936. doi: 10.1037/0096-1523.29.5.919
127. Young, D. A., & Freyslinger, M. G. (1995). Scaffolded instruction and the remediation of Wisconsin Card Sorting Test deficits in chronic schizophrenia. Schizophrenia Research, 16, 199-207.
128. Zylberberg, A., Dehaene, S., Roelfsema, P. R., & Sigman, M. (2011). The human Turing machine: A neural framework for mental programs. Trends in Cognitive Sciences, 15, 293-300. doi: 10.1016/j.tics.2011.05.007